This series of files compile analyses done during Chapter 2.

All analyses have been done with R 4.0.2.

Click on the table of contents in the left margin to assess a specific analysis.
Click on a figure to zoom it

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1. Ecological Quality Status

When relevant, we calculated an Ecological Quality Ratio (EQR) as established by the WFD and MSFD (which varies between 0 and 1). This ratio is calculated with the following equation:

\[ EQR = \frac{V_{ind} - Ref_{bad}}{Ref_{good} - Ref_{bad}} \]

  • \(V_{ind}\) is the value of an indicator at a certain location
  • \(Ref_{bad}\) is the reference value for a “bad” status
  • \(Ref_{good}\) is the reference value for a “good” status

This ratio is then classed into Ecological Quality Status categories, where reference values and limits for class transitions are specific to each indicator. Five classes are typically described:

  • bad (red #FF0000)
  • poor (orange #FFA500)
  • moderate (yellow #EEEE00)
  • good (green #228B22)
  • high (blue #0000EE)

We calculated this ratio using different indicators, in order to compare their efficiency and relevance.

Specific richness

We defined class thresholds with 20 %, 40 %, 60 and 80 % of the maximal specific richness.

Shannon index

We defined class thresholds with 20 %, 40 %, 60 and 80 % of the maximal Shannon index.

Margalef index

We defined class thresholds with 20 %, 40 %, 60 and 80 % of the maximal Margalef index.

Simpson index

We defined class thresholds with 20 %, 40 %, 60 and 80 % of the maximal Simpson index.

BOPA

We defined class thresholds using the method from Dauvin & Ruellet (2007).

AMBI

We defined class thresholds using the methods from Borja et al. (2000) and Muxika et al. (2005).

M-AMBI

We defined class thresholds using the method from Muxika et al. (2007).

BENTIX

We defined class thresholds using the method from Simboura & Zenetos (2002).

2. Relationships between indicators and abiotic parameters

In this section, we study the statistical relationships between indicators calculated above and different abiotic parameters, in order to understand how well they can be used to detect perturbations.

2.1. Covariation

Several types of models were considered to explore relationships: linear, quadratic, exponential and logarithmic. The model with the highest \(R^{2}\) is presented on each plot.

⚠️ Only linear models were implemented for now, as there are some bugs with the calculation of the others.

Specific richness

Total density

Total biomass

W statistic

Shannon index

Margalef index

Simpson index

Pielou evenness

Taxonomic diversity

Functional richness

Functional evenness

Functional divergence

AMBI

M_AMBI

BOPA

BENTIX

2.2. Correlation

Correlations have been calculated with Spearman’s rank coefficients.

Correlation coefficients between habitat parameters and indices
  S N B W H margalef lambda J delta FR FE FD BOPA AMBI M_AMBI BENTIX
om -0.026 -0.121 0.012 0.065 0.122 0.037 0.133 0.136 0.059 -0.109 -0.034 0.066 0.184 -0.167 -0.038 0.305
gravel 0.029 0.007 0.154 0.139 0.012 0.025 0.039 0.07 0.074 0.242 0.079 -0.139 -0.017 0.054 0.056 -0.17
sand 0.059 0.084 -0.061 0.039 0.031 0.031 0.027 -0.027 0.081 0.08 0.049 0.075 -0.28 0.199 0.083 -0.305
silt -0.054 -0.013 0.026 -0.088 -0.068 -0.057 -0.086 -0.055 -0.139 -0.117 -0.091 0.002 0.301 -0.17 -0.074 0.279
clay -0.098 -0.076 -0.02 -0.037 -0.044 -0.086 -0.027 0.02 -0.059 0.028 -0.067 -0.08 0.07 -0.05 -0.048 0.02
arsenic -0.266 -0.149 -0.165 -0.203 -0.193 -0.231 -0.149 0.008 -0.125 -0.275 -0.137 0.017 0.266 -0.036 -0.253 0.136
cadmium -0.308 -0.042 -0.173 -0.339 -0.291 -0.322 -0.252 -0.133 -0.275 -0.294 -0.246 0.163 0.237 -0.019 -0.246 0.084
chromium -0.331 -0.167 -0.14 -0.242 -0.273 -0.311 -0.228 -0.041 -0.222 -0.353 -0.173 0.056 0.287 -0.021 -0.271 0.163
copper -0.298 -0.172 -0.145 -0.194 -0.225 -0.276 -0.187 -0.025 -0.199 -0.336 -0.171 0.109 0.252 -0.018 -0.246 0.183
iron -0.377 -0.273 -0.047 -0.176 -0.251 -0.322 -0.199 0.034 -0.171 -0.385 -0.112 0.057 0.248 0.004 -0.324 0.09
manganese -0.287 -0.096 -0.084 -0.241 -0.261 -0.3 -0.227 -0.085 -0.245 -0.314 -0.23 0.069 0.333 -0.031 -0.242 0.162
mercury -0.234 -0.084 -0.016 -0.165 -0.199 -0.237 -0.173 -0.075 -0.228 -0.307 -0.194 0.145 0.269 -0.043 -0.191 0.169
lead -0.304 -0.135 -0.155 -0.24 -0.252 -0.29 -0.213 -0.051 -0.216 -0.312 -0.197 0.097 0.301 0.007 -0.261 0.124
zinc -0.32 -0.145 -0.165 -0.252 -0.253 -0.303 -0.212 -0.056 -0.221 -0.336 -0.188 0.161 0.26 -0.01 -0.274 0.15
p-values of correlation test between habitat parameters and indices
  S N B W H margalef lambda J delta FR FE FD BOPA AMBI M_AMBI BENTIX
om 0.7858 0.2113 0.9048 0.5035 0.2093 0.7012 0.1701 0.1614 0.544 0.2628 0.7284 0.4963 0.05618 0.08394 0.6975 0.001327
gravel 0.7662 0.9425 0.1117 0.1512 0.9048 0.7948 0.6896 0.4692 0.4459 0.01174 0.4148 0.1513 0.8576 0.5795 0.5624 0.07776
sand 0.5414 0.3846 0.5339 0.6851 0.752 0.7485 0.7805 0.7828 0.4045 0.4099 0.6168 0.4429 0.00334 0.03891 0.396 0.001331
silt 0.581 0.8963 0.7923 0.3672 0.4834 0.5607 0.3744 0.5692 0.1509 0.2264 0.3508 0.9844 0.001537 0.07875 0.4489 0.003456
clay 0.3134 0.4336 0.8338 0.7052 0.6486 0.3749 0.7849 0.8392 0.5453 0.7737 0.4939 0.4117 0.4685 0.6054 0.622 0.8405
arsenic 0.005376 0.1238 0.08845 0.03544 0.04503 0.01607 0.1243 0.9356 0.1991 0.003949 0.1586 0.8603 0.005411 0.7147 0.008325 0.1592
cadmium 0.001171 0.6656 0.07386 0.0003388 0.002263 0.0006803 0.008567 0.1701 0.00396 0.002036 0.01018 0.09262 0.01348 0.8475 0.0102 0.3893
chromium 0.0004702 0.08459 0.1476 0.01158 0.004269 0.001052 0.01769 0.6766 0.02095 0.0001797 0.07347 0.5643 0.002598 0.8322 0.004615 0.09147
copper 0.001731 0.07495 0.1337 0.04442 0.0195 0.003892 0.05222 0.8009 0.03895 0.0003739 0.07738 0.2608 0.008463 0.8572 0.01035 0.05769
iron 5.82e-05 0.004321 0.6322 0.06932 0.00892 0.0006646 0.03854 0.725 0.07715 3.87e-05 0.2485 0.5548 0.009583 0.9679 0.0006306 0.3544
manganese 0.00256 0.3224 0.3861 0.01201 0.006285 0.001592 0.01793 0.3814 0.01088 0.0009191 0.01642 0.4765 0.0004345 0.7532 0.01147 0.09382
mercury 0.0146 0.3891 0.8673 0.08806 0.03882 0.0135 0.07408 0.4404 0.01764 0.001231 0.04375 0.1336 0.004795 0.6622 0.04821 0.07972
lead 0.001371 0.1643 0.109 0.01223 0.00859 0.002352 0.02692 0.5997 0.02485 0.00101 0.04112 0.3171 0.001546 0.9397 0.006309 0.2018
zinc 0.0007423 0.1333 0.08864 0.008563 0.00819 0.001442 0.02772 0.5628 0.02152 0.0003788 0.0519 0.09615 0.00654 0.9183 0.004152 0.1206

3. Bootstrap for estimating indicator robustness

In this section, we are calculating values of the indicators for shallow and deep stations using a bootstrap method, so that we have an idea of the robustness of each measure.

3.1. Shallow stations

Bootstrap results for shallow stations
  True mean Bootstrap Mean bias Boostrap 95% CI
S 9.192 -0.03569 [9.1708;9.2852]
N 138.7 -0.3769 [136.8829;141.2555]
B 7.352 -0.05717 [7.1399;7.6789]
W 0.0109 -0.0164 [0.0269;0.0277]
H 1.353 -0.006664 [1.3537;1.3663]
margalef 1.926 -0.01379 [1.9302;1.949]
lambda 0.6202 -0.002792 [0.6205;0.6255]
J 0.6564 -0.00366 [0.6572;0.663]
delta 51.66 -0.3568 [51.7936;52.2472]
FR 23.35 -3.171 [26.111;26.9268]
FE 0.5542 0.002318 [0.5495;0.5543]
FD 0.7656 -0.007396 [0.7701;0.7759]

3.2. Deep stations

Bootstrap results for deep stations
  True mean Bootstrap Mean bias Boostrap 95% CI
S 13.99 -0.009295 [13.964;14.0302]
N 89.13 -0.1556 [88.8068;89.7726]
B 8.72 0.0243 [8.5538;8.838]
W 0.02522 -0.007476 [0.0325;0.0329]
H 1.952 0.0002019 [1.9489;1.9551]
margalef 3.046 -7.372e-05 [3.0402;3.052]
lambda 0.7699 0.0001827 [0.7687;0.7707]
J 0.7601 0.0003165 [0.7588;0.7608]
delta 63.48 0.01433 [63.386;63.5518]
FR 31.76 -7.59 [38.827;39.8772]
FE 0.6324 -0.001514 [0.6331;0.6347]
FD 0.8282 0.01091 [0.8165;0.8181]

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